Background
The study of biological pathways has become a central topic in molecular systems biology 1. While the precise definition of "biological pathway" is still debatable, most researchers regard a biological pathway as a series of inter-connected cellular events among biomolecular entities. A biological pathway can be activated by extracellular stimuli and lead to persistent changes of the biochemical state of cells. There are three major types of molecular pathway events (or, events for brevity) that define biological pathways:
• Signal transduction events. Common in signalling pathways (e.g., Wnt signaling pathway 2), these events define the interactions among molecular entities during signal transduction cascades, i.e., how external stimuli such as molecules in the cellular environment are transduced into intracellular molecular signals that are relayed among different cellular organelles. Examples of signal transduction events in signalling pathways are protein-protein interactions, protein post-translational modifications, protein translocations, and protein complex formations/dissociations.
• Enzymatic reaction events. Common in metabolic pathways (e.g., glycolysis pathway), these events define chemical reactions that metabolites (as either substrates or products) and catalytic enzymes are involved in. Examples of enzymatic reaction events are catabolic reactions (breaking down of larger molecules to produce energy) and anabolic reactions (synthesis of cellular components from smaller molecules).
• Genetic regulation events. Common in genetic regulatory pathways (e.g., usually abbreviated as regulatory pathways), these events define the dependent relationships between regulatory entities, e.g., a transcription factor that binds to specific DNA binding motifs, and target entities, and a gene whose transcription is being regulated by a transcription factor. In addition to gene regulation events, regulatory pathways may also include sRNA and sRNA target gene regulation.
Collecting and modeling biological pathways are critical for interpreting "Omics" data 3. For example, pathway knowledge has been used to identify new functional modules from gene expression profiles 45 and relate gene mutations to one another in polygenic diseases such as breast cancer 6. The development of biological pathways can also help build disease biology models, from which new hypotheses of targeted drugs and robust biomarkers may be developed. For example, molecular entities in FGFR1/PI3K/AKT signaling pathways, the Akt/PKB pathway, the Met pathway, and the Wnt signaling pathway have all been extensively investigated as potential cancer drug targets 78910. Novel drug discovery strategies to screen small molecules based on an entire pathway instead of particular protein targets can also be developed by designing global disease-related pathway inhibitors 11. Pathway studies have also shown promise in molecular diagnostic applications, e.g., identifying efficacy and toxicity biomarkers 12, and building new multi-marker panels to improve prediction of disease prognosis and development of treatment plans 13. Ongoing efforts to represent, develop, and apply pathway models will be crucial for future genome medicine and personalized medicine applications 1415.
While there are approximately 300 biological pathway-related online resources reported by Pathguide http://www.pathguide.org/today, these resources have been developed with variable degrees of data coverage, quality, and utility 1. Examples of high-quality biological pathway database resources are: SPAD 16, CST 17, STKE 18 and COPE 19 for signaling pathways; TRANSFAC 20 for regulatory pathways; and KEGG 21, WIT 22, ExPASy 23, UM-BBD 24 and HumanCyc 25 for metabolic pathways. In addition, new databases such as HPRD 26, HAPPI 27, and STRING 28 have been developed to provide available high-throughput protein-protein interaction data to help fill gaps in rapidly growing molecular signaling pathway data. Recent efforts to expand biological pathway coverage beyond a single pathway event type have also been reported, e.g., NCI-PID 29, Reactome 30, BioCarta 31, Pathway Commons 32, Panther 33, Protein Lounge 34 and WikiPathways 35. However, by comparing the coverage of high-quality protein-protein interactions from the HAPPI database 27 with annotated human pathways documented from the Reactome database, for example, it is not difficult to conclude that current coverage of known human biological pathway events is 1–2 orders of magnitude smaller than the theoretical maximum that can be defined by all known reliable human protein-protein interactions. Therefore, many pathway biology studies begin by expanding biological pathway data coverage and building high-quality integrative pathway models.
The most reliable approach to expanding human pathway data coverage without sacrificing data quality continues to be database integration. While there are several computational techniques that can help predict metabolic pathways 36, regulatory pathways 3738, and signaling pathways 39, they all have limited applicability and are thus beyond the scope of this work. However, integrating biological pathway from different data sources has been challenging, due to the heterogeneity in pathway data formats, representation schemes, and retrieval methods. For example, at the syntactic level, while many pathway databases such as the NCI-PID 29, Reactome 30, and KEGG 21 provide both molecular component and molecular interaction data as XML documents, Protein Lounge 34 and BioCarta 31 provide pathway details (including molecular entities and pathway events) only in TXT file and embedded pathway diagrams. Pathway ontology standards such as PSI-MI 40 or BioPAX 41 or GPML 42 can help resolve syntactic level data heterogeneity; however, these standards are relatively new and are available only in a few recent systems such as cPATH 43, NCI-PID 29, Reactome 30 and WikiPathways 35. At the semantic level, incompatible pathway names, event representations, and molecular entity identifiers also poses challenges in querying pathway information across pathway data sources, particularly those with complementary information. Pathway names from different pathway data sources for the same pathway often differ slightly and therefore are poor choices as identifiers. Identifying pathways directly using pathway molecular entities can also be problematic, because the ensemble of molecular entities referring to the same pathway may vary among different annotation sources. Pathway molecular entities may be referred to with any public sequence identifier, which includes RefSeq ID, HGNC symbol, GenBank accession, SwissProt ID, UniProt name, KEGG ID, or IPI number. Furthermore, different databases may choose to provide available pathway information at different levels of molecular detail, e.g., with protein post-translational modification status, protein complex association status, or cellular location information. In summary, pathway data incompatibility at both the syntactic and semantic levels has inhibited the growth of high-quality integrative pathway data sources.
In this work, we describe the development of a new online integrated pathway database resource, the Human Pathway Database (HPD). HPD is an ongoing pathway data warehousing project, in which we integrate all three types of human pathway data and compile additional detailed information on pathway genes, proteins, metabolites, protein complexes, and pathway events. The concept of developing an organism-specific integrated pathway database resource is not unique, e.g., MAtDB 44 for managing all biological pathways for Arabidopsis and FlyMine 45 for managing both functional genomics and pathway data for Drosophila. Applying semantic-level data integration techniques, we collect, represent, and manage human-specific pathway data in HPD based on information from NCI-PID, Protein Lounge, KEGG, BioCarta, and Reactome databases. HPD provides a comprehensive view of current human biological pathway data, which consists of a total of 999 pathways and 59,341 molecular entities. Online HPD users may search the database for all relevant pathway information related to query protein(s), identify all pathways involving a query protein(s), and examine details related to pathway components, molecular events, and related pathways. Using three case studies, we show how to take advantage of HPD online and backend database querying capabilities to manage, query, and compare different types of biological pathways for systems biology studies. HPD is freely available online at http://bio.informatics.iupui.edu/HPD.
Results
Database content statistics
By integrating human biological pathway data from five major curated sources, we have developed HPD, a human pathway data warehouse. As of the current release, HPD contained a total of 999 human pathways that cover all three major types of pathway events. These pathways cover 59,341 molecular entities and 16,271 pathway events. As of April 2009, HPD contains the highest pathway data coverage among all human biological pathway databases publically available. Since HPD does not contain new pathways derived computationally, the quality of the database remains the same as that of each pathways curated from their respective source databases. A comparison of human pathways in HPD against several common human pathway data sources is shown in Table 1. Top 100 pathways, genes/proteins and compounds are listed in the additional file 1.
<p>Additional file 1</p>
This additional file lists top 100 pathways ranked by degree (number of neighbour pathways, with which similarity score > 0); top 100 genes/proteins ranked by frequency, and top 100 compounds ranked by frequency. Here the frequency of a molecule entity (i.e. gene/protein or compound) also includes times appearing in same pathways.
Click here for file
<p>Table 1</p>A comparison of human pathways in HPD against several common pathway data sources.
BioCarta
1
HumanCyc
2
KEGG
3
Reactome
4
PID
5
Protein Lounge
6
HPD
Quality
Scope of Content
Metabolic and signaling pathways
Metabolic Pathways
Metabolic, Regulatory, signaling, disease and drug pathways
Metabolic, signaling and regulatory pathways
Signaling and regulatory pathways
Metabolic, signaling and regulatory pathways
Metabolic, signaling and regulatory pathways
Curation Type
Manual
Manual and Computational Prediction
Manual
Manual
Manual
Manual
Integrated from Manually curated database
Coverage
Human Pathways
354
327
205
960
87
427
999
Events
> 3000
> 1500
4149
3203
5232
NA
16271
Functionality
Related Pathways
No
No
Yes
No
No
No
Yes
Multiple Protein Search
No
No
No
No
Yes
No
Yes
Pathway-Protein Association Table
No
No
No
No
No
No
Yes
Pathway-Pathway Similarity Network
No
No
No
No
No
No
Yes
1 Data from http://www.biocarta.com (as of April 2009)
2Data from http://humancyc.org/ (as of October 2008)
3Data from http://www.genome.jp/kegg/ (as of March 2009)
4Data from http://www.reactome.org (as of April 2009)
5Data from http://pid.nci.nih.gov/ (as of April 2009)
6Data from http://www.proteinlounge.com (Commercial Version, as of April 2009)
** The count of HPD pathways has 39 pathways more than reactome since HPD utilizes 2004 Reactome (version 22 release). The next version would include the recent version from Reactome
Scale distributions of integrated HPD pathways
Pathway scale can reflect the integrality of information needed for a biological topic. Here, we define pathway scale as the number of entities (nodes, including gene, protein, complex and metabolite) or events (edges, including interaction, reaction and regulation) involved in a pathway. We performed a statistical analysis on Pathway Scale Distribution (PSD) in the whole HPD, shown in Figure 1, from which we can see that the PSD defined by entity in Figure 1a is almost the same as the PSD defined by event in Figure 1b. This result indicates that the ratio of entity (node) number and event (edge) number in HPD pathways is almost fixed, which implies that the quality of HPD is consistent. We can also find that the PSD defined by gene/protein in Figure 1a is much closer to the PSD defined by entity or event than the PSD defined by metabolite, interaction, reaction and regulation, which suggest that using gene/protein number can represent pathway scale more precisely. This is the most important evidence not only for the implemental definition of pathway scale, but also for the definition of pathway-pathway similarity, both of which can be defined by the number of the Uniprot IDs mapped from genes or proteins in a pathway.
<p>Figure 1</p>Pathway scale distributions for HPD molecular entity data and molecular event data
Pathway scale distributions for HPD molecular entity data and molecular event data. A pathway scale refers to the number of molecular entities or molecular events involved in a given pathway. The frequency on the y-axis refers to the count of all pathways falling in the category of a particular pathway scale size on the x-axis. (a) Distributions of pathway scale by counting molecular entities. (b) Distributions of pathway scale by counting molecular events.
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We can also notice that, since entities in a pathway here also include protein complexes, each of which will only count as one entity, the PSD defined by that entity is a little bit lower than the PSD defined by gene/protein in Figure 1a. Both of the results in Figure 1a and 2b suggest that the integration process of HPD is successful by considering pathway scales, but either small pathways or large pathways may still be under-represented in the whole HPD.
<p>Figure 2</p>An overview core functionality of the online HPD software
An overview core functionality of the online HPD software. (a) The HPD home page showing a query protein input box (supports multiple protein search using either gene names or UniProt identifiers). (b) A Web page containing the list of pathways retrieved as a result of a query protein input. Links to obtain pathway-pathway similarity matrix and pathway-protein association matrix and to download query-retrieved pathways were also shown on the Web page. (c) An advanced search page in which users may search pathways using gene name(s), with any term that appears within pathway names or with a list of one or more HPD Pathway IDs. This search retrieves a page similar to in (b). (d) A hyperlinked Web page showing pathway detailed information on molecular entities (Proteins, Complexes and Compounds) within the query pathway. (e) A hyperlinked Web page showing detailed pathway information on pathways similar to the query pathway. (f) A hyperlinked Web page showing detailed pathway information on molecular events (Interaction/Reaction/Regulation) within the query pathway. (g) A hyperlinked Web page showing pathway image link and reference articles.
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General online features
In Figure 2, we show the user interfaces of the Web-based online version of HPD. It supports both standard and customized user search options that allow them to specify a list of genes/proteins or keywords as the query input. Upon executing the queries, HPD can retrieve a list of related human pathways in an HTML table, with which users can further explore pathway details by clicking the hyperlink on a pathway ID in the table. In the pathway detail HTML table that pops up, all listings of molecular entities, events, related pathways, and reference resources of a specific pathway are shown. Users can also directly interact with advanced HPD features; by selecting the pathway-protein association matrix applet (See Figure 3 for an example) and the pathway-pathway similarity matrix applet (See Figure 4 as an example) Comprehensive hyperlinks were built so that users can search for new pathways based on visual analysis performed on the applets. User queried pathway data stored in HPD can also be downloaded as flat files without restriction to Academic users.
<p>Figure 3</p>A BRCA1-retrieved HPD pathway-protein association matrix
A BRCA1-retrieved HPD pathway-protein association matrix. The matrix shows few HPD pathways involving the query protein BRCA1 (on the y-axis) against pathway molecular components (on the x-axis). A red cell in the matrix indicate that the molecular entity is present in the pathway, whereas a white cell in the matrix indicate that the molecular entity is absent from the pathway. Only few HPD pathways are shown. These BRCA1-retrieved pathways are sorted by their shared protein counts among all pairwise pathway comparisons in these pathways.
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<p>Figure 4</p>A BRCA1-retrieved HPD pathway-pathway similarity matrix
A BRCA1-retrieved HPD pathway-pathway similarity matrix. This is an interactive heat map containing similarity scores among the pathways involving query protein(s) or gene name(s). The tooltip shows the two pathway IDs and names corresponding to the particular cell pointed to along with their similarity score. A right click context menu shows the links (HPD_644 and HPD_340) to a Web page containing pathway information (as shown in Figure 2d). The "Compare HPD_644 against HPD_340" option will redirect to a page with a pathway-protein matrix showing proteins shared by these two pathways. The legend above the map indicates the range of similarity score (0 to 1).
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Case studies
To demonstrate the capabilities of HPD, we show three case studies of increasing complexity and biological significance to demonstrate how HPD could be used to solve real-world biological pathway problems.
Case study 1: searching for biological pathways and their components based on a single query protein
Using the standard query box provided at the HPD home page, we can search HPD for all biological pathways involving BRCA1_HUMAN (a major protein involved with breast cancer susceptibility). HPD returns a list of the top 20 BRCA1-related pathways, which are ordered by decreasing number of proteins that each pathway shares among all pathway pairs from retrieved pathways. The better the rank a retrieved pathway has, the more related it should be to both the query protein BRCA1 and all BRCA1-relevant pathways. In this list, highly-ranked pathways such as "Molecular Mechanisms of Cancer", "P53 Signaling", "DNA Repair Mechanism", and "BRCA1 pathway" are all well characterized signaling pathways in breast cancer. All pathways are hyperlinked to their own detailed pathway information pages, which include molecular entities (proteins, complexes and metabolites), related pathways, events, and external pathway images and reference articles. (See Figure 2 for details).
The Web page with the list of pathways related to BRCA1 also contains links to download data. Four types of data, pathway list, pathway-protein association matrix, and pathway-pathway similarity scores are downloadable as flat files.
Note that the pathway-protein association matrix contains proteins that are involved in the top 20 pathways retrieved based on the single protein query, sorted according to their descending maximal pathway involvement by activity count. BRCA1 related proteins are retrieved by pathway, with each of the proteins covered by at least two of the 20 pathways. A close examination reveals that many breast cancer susceptibility genes including BRCA1, BRCA2, P53, PCNA 46, FOXA1 47 and STK6 48 from recent individual studies and breast cancer biomarker genes such as ERBB2, FGFR2, M3K1, and PTEN 4950, have all been found in this list.
Particularly noteworthy is the Applet in the HPD Web page that shows all the query-related biological pathways with involved proteins in a heat map. In Figure 3, BRCA1 related pathways and involved proteins are sorted and used as two separate dimensions of the matrix. Mousing over a color-filled cell invokes an applet tooltip message, which shows the pathway and protein names.
HPD users can also visualize the pathway-pathway similarity matrix (Figure 4) which shows the similarity score among the BRCA1 related pathways. The pathway-pathway similarity matrix allows users to visualize a cluster of similar pathway pairs as a 2-D interactive heat map. This heat map allows users to right click on any cell (shown in Figure 4) to compare pathway pair on the heat map (future versions will include multiple pathway selection) by looking at the pathway-protein association matrix. This facilitates better understanding for deriving novel pathways most similar to BRCA1 related pathways.
Case study 2: developing pathway-pathway similarity networks from heterogeneous data sources
Using the advanced HPD search function online, a user can specify multiple proteins as the query input to obtain a list of most relevant pathways related to the query protein set. For example, if the user enters "BRCA1_HUMAN, FOXA1_HUMAN, STK6_HUMAN" as query inputs, a significant number of pathways (Table 2) related to any of the query protein inputs will be returned. To ensure retrieved pathways are relevant to the query protein inputs and to avoid overly restrictive filtering of related pathways (e.g., requiring all pathways retrieved to contain all proteins in the input query would be too restrictive), we can use the concept of pathway similarity (see Methods section for details) and apply a minimal pathway similarity threshold {Si, j ≥ 0.2, and |Pi ∩ Pj| > 2}, i = 1...N, j = 1...N. The threshold indicates at least 20% minimal shared molecular entities with no fewer than 2 shared entities between two pathways. After applying this filter, 25 pathways and 39 pathway pairs are retrieved.
<p>Table 2</p>A list of HPD pathways retrieved by the query BRCA1.
Pathway_ID
Pathway_Name
Source_Name
Shared_Proteins
HPD_786
Molecular Mechanisms of Cancer
Protein Lounge
264
HPD_953
JAK/STAT Pathway
Protein Lounge
239
HPD_1182
p53 Signaling
Protein Lounge
76
HPD_1567
DNA Repair Mechanism
Protein Lounge
69
HPD_336
Chks in Checkpoint Regulation
Protein Lounge
52
HPD_596
BRCA1 Pathway
Protein Lounge
44
HPD_1058
Aurora A signaling
NCI-Nature Curated
43
HPD_1070
BARD1 signaling events
NCI-Nature Curated
40
HPD_788
ATM Pathway
Protein Lounge
33
HPD_644
role of brca1 brca2 and atr in cancer susceptibility
BioCarta
27
HPD_988
Ubiquitin mediated proteolysis
KEGG
25
HPD_12
cell cycle: g2/m checkpoint
BioCarta
23
HPD_628
atm signaling pathway
BioCarta
20
HPD_340
GADD45 Pathway
Protein Lounge
19
HPD_752
Fanconi's Anaemia Pathway
Protein Lounge
17
HPD_548
DNA Damage Induced 14-3-3Sigma Signaling
Protein Lounge
14
HPD_212
brca1 dependent ub ligase activity
BioCarta
11
HPD_1261
FOXA1 transcription factor network
NCI-Nature Curated
10
HPD_707
Recruitment of repair and signaling proteins to double-strand breaks
Reactome
9
HPD_276
ATM mediated phosphorylation of repair proteins
Reactome
5
In Figure 5, we show a visual display of the pathway-pathway similarity network, using pathway similarity scores retrieved from HPD using ProteoLens 51. In order to generate a comprehensive perspective of breast cancer pathways seeded with the three initial query proteins, all five types of data sources have been used. This observation strengthens the claim for the necessity of integrating pathways from heterogeneous sources. HPD pathways in this case study provide a good meta-model that connects our fragmented pathway knowledge together in pathway-pathway similarity networks. This global perspective, supported by integration of otherwise incompatible pathways from different sources, enhances the chance of exposing novel insights in the search for disease drug targets and biomarkers.
<p>Figure 5</p>A breast cancer-specific pathway-pathway similarity network
A breast cancer-specific pathway-pathway similarity network. In this pathway-pathway similarity network, 25 HPD pathways derived from different sources are shown. The color and shapes in the diagram were drawn to indicate original HPD pathway data sources, based on the shape/color legend shown in the upper left corner. The subnetwork pathways and host pathways are also indicated with directed cyan edges. Edges are labeled with the number (in red) of molecular entities shared by the connected pathways. The count of molecular entity overlap between each pair of related pathways is labeled as red-colored numbers on the edge. Only pathway pairs with a similarity score and overlap above the threshold {Si, j ≥ 0.2, AND |Pi ∩ Pj| >2} are shown.
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In Figure 6, we show a comparison of using the "Multiple Protein Search" feature among three databases: HPD, KEGG, and Panther. Three gene names BRCA1, FOXA1, and AURKA were used to build a common query gene set. The KEGG Genes database was manually searched and only one KEGG Pathway was found, using the "Search object in Pathways" functionality of KEGG (actual corresponding KEGG gene ID entered: hsa:672, hsa:3169, and hsa:6790). Panther had a "Batch ID search" which accepted the three gene symbols and retrieved only four unique pathways. HPD not only retrieved more pathways (n = 25), but also supported multiple identifier types as inputs, e.g., UniProt names.
<p>Figure 6</p>A comparison of HPD "Multiple Protein Search" feature with those in Panther and KEGG
A comparison of HPD "Multiple Protein Search" feature with those in Panther and KEGG. The example shows that different pathway databases retrieved different numbers of pathways. Three genes (AURKA, BRCA1, and FOXA1) were used to build this example. The KEGG database returned only one pathway in the result. Panther returned four pathways. HPD returned 25 pathways. The difference is primarily due to the use of "similar pathways" concept to allow retrieval of pathways that matched only partial gene/protein list.
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Case study 3: developing integrated pathway models from heterogeneous sources
While pathway-pathway similarity networks are useful for generating global perspectives on the relationships between pathways, the next case study demonstrates how to connect different types of biological pathways within HPD to form integrated pathway networks. Since pathway data managed at HPD is integrated at the schematic level, "deep integration" and "deep integrative analysis" are possible. We will use two breast cancer-related proteins, BRCA1_HUMAN and FOXA1_HUMAN, as an example. According to the HPD data model (See additional file 2 for details), the table Connect_mol_updated contains mappings among pathways, interactions, and molecules. To search for all related pathways containing the above two proteins within the HPD data warehouse, we can execute the following SQL query:
<p>Additional file 2</p>
This additional file describes the pathway entity-relationship (ER) data model for HPD pathway integrations in detail.
Click here for file
SELECT pathway_name,mol_in, Mol_In_updated, name_in, Mol_out,
Mol_Out_updated, name_out, interaction_type, SYS_CONNECT_BY_PATH(Mol_In, '/') "Path"
FROM connect_mol_updated
START WITH name_in = 'BRCA1_HUMAN'
CONNECT BY nocycle PRIOR Mol_Out_updated=Mol_In_updated
and level < 3
INTERSECT
SELECT pathway_name,mol_in, Mol_In_updated, name_in, Mol_out,
Mol_Out_updated, name_out, interaction_type, SYS_CONNECT_BY_PATH(Mol_In, '/') "Path"
FROM connect_mol_updated
START WITH name_in = 'FOXA1_HUMAN'
CONNECT BY nocycle PRIOR Mol_Out_updated=Mol_In_updated
and level < 3;
We organize the results and present our final pathway analysis results in Figure 7, which shows many relationships not found in individual fragmented biological pathways separately. The FOXA1 transcription factor network contains 9-cis-Retinoic acid which regulates FOXA1 (Hepatocyte nuclear factor 3-alpha) 52; it also contains BRCA1 (Breast cancer type 1 susceptibility protein) and CYP2C18 (Cytochrome P450 2C18), which is positively regulated by FOXA1 53. Arachidonic acid metabolism from KEGG PATHWAY involves arachidonic acid, which can be catalyzed by CYP2C18 54 to produce 14,15-epoxy-5,8,11-eicosatrienoic acid. This intermediate product can be further catalyzed by EPHX2 (Epoxide hydrolase 2) to produce 14,15-dihydroxyeicosatrienoic acid 55. In BioCarta, ATM signaling pathway involves BRCA1, which positively regulates RAD51 which regulates DNA Repair 56. The "Presynaptic phase of homologous DNA pairing and the strand exchange" pathway of Reactome contains BRCA2, which binds with RAD51 to form the RAD51:BRCA2 complex 57. Human protein-protein interactions data could also be retrieved and show that EPHX2 interacts with NSDHL (Sterol-4-alpha-carboxylate 3-dehydrogenase, decarboxylating). Phosphatidylinositol-3,4,5-triphosphate (PIP3), a lipid molecule generated by the action of phosphoinositide-3-kinase (PI3K), can be induced by a variety of stimuli. PIP3 is thought to be the major physiological substrate for PTEN, a phosphatase that can dephosphorylate many phosphatidyl inositides, which has been implicated in tumorigenesis 58. Activation of protein kinase B (PKB)/Akt contribute to resistance to antiproliferative signals and breast cancer progression in part by impairing the nuclear import and action of p27 (CDKN) 59.
<p>Figure 7</p>An integrated pathway model involving FOXA1 in breast cancer
An integrated pathway model involving FOXA1 in breast cancer. The figure shows how information from different pathway database sources are readily integrated, queried, and analyzed together in HPD for FOXA1-related breast cancer signaling studies.
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The integrated pathway model based on HPD pathways can be used as an investigative tool for disease diagnostic and therapeutic applications. For example, 9-cis-Retinoic acid is recognized as a possible breast cancer biomarker 60 and FOXA1 has gained increasing attention as a possible breast cancer therapeutic target 61. The BRCA2-RAD51 interaction is essential for DNA repairs and has also been suggested as a novel target for anti-breast cancer drugs 62. In addition to breast cancer, links between breast cancer and other diseases can be studied. For example, increased risk of hereditary prostate cancer is known to be a result of polymorphism in the CDKN1B (p27) gene 63. Epoxide hydrolase 2 has been characterized as a key mediator molecule in hypertensive, cardiovascular, inflammatory, pulmonary, and diabetic-related diseases 646566. CHILD syndrome, an X-linked dominant trait with lethality for male embryos, can also be traced to mutations in NSDHL, a gene playing crucial roles in the cholesterol biosynthetic pathway 67.
Through this case study, we have shown the significance of integrating pathway information from different types and data sources. The interconnected network analysis offers researchers a rare opportunity to gain global perspectives on events previously perceived in isolation. This "deep integrative analysis" opportunity cannot be readily obtained by using multiple online pathway databases. For example, NCI Nature Curated Pathway Interaction Database has a 'Connected Molecules' functionality, which may only be used to find molecular connections within the same pathway data source. In all, the convenience of building new integrative pathway models with the new HPD may greatly facilitate new drug development and biomarker discovery.
Conclusion
We developed HPD as an integrated pathway database system to manage, query, and analyze human biological pathways. HPD integrates all three types of biological pathways from five heterogeneous pathway database sources at syntactic, semantic, and schematic levels, primarily based on data warehousing techniques driven by a unified pathway data model. Pathway molecules, interactions, chemical reactions, and similar pathways can be searched, displayed, and downloaded from a unified online user interface. The current HPD software can help users address a wide range of pathway-related questions in human disease biology studies.
While the human Reactome is still far from complete, an integrative pathway database such as HPD has the capability to help researchers establish a global perspective necessary for understanding molecular mechanisms and develop biomedical applications. We will further expand the database to include pathways from HumanCyc 25, Wikipathways 35, NetPath 68, Panther 33 and TRANSFAC 20. We also plan to integrate protein-protein interaction data from HAPPI 27 with the aim of discovering novel pathways when combined with HPD. Additional functions will also be provided such as pathway reconstruction where users can select pathways and derive a reconstructed pathway expanded with protein-protein interaction data. With ongoing efforts, HPD can become a useful resource, linking proteins, genes, RNAs, signaling reactions, and gene regulatory events for systems biology applications.
Methods
Pathway data sources
We show an overview of the data integration process in Figure 8. Pathway data in HPD were collected or indexed from five different sources, i.e., NCI-Nature Curated data 29, BioCarta 31, Protein Lounge 34, Reactome 30 and KEGG 21. The NCI-Nature Curated, Reactome and BioCarta data sets were all downloaded from Nature pathway interaction database Website and kept updated as of April 2009 release of the production HPD Website. In particular, the NCI-Nature Curated pathways are curated by Nature Publishing Group editors based on known biomolecular interactions and key cellular processes of signaling/regulatory pathways. The Reactome database was downloaded in December 2007 (Release version 22). Pathway molecules from both NCI-Nature and Reactome were identified by their UniProt identifiers and annotated with post-translational modification information. Pathway molecules from BioCarta were identified by Entrez Gene IDs without post-translational modification annotations. In all three data sets, each pathway was represented as a series of events, each of which consists of molecules in one of the following four roles: input molecule, output molecule, agent, and inhibitor. Content from the Protein Lounge was indexed by a web crawler accessing a publicly available Web site. The crawled content was verified with that provided as a site license to the authors and other users at Indiana University Simon Cancer Center. Since Protein Lounge is a commercial database that contains curated signaling, transduction, and metabolic pathways, we chose to index instead of integrate the full content into the data warehouse, which only indexed pathway-involving protein IDs and references to pathway diagram drawings. Original pathway molecules derived from Protein Lounge were identified by RefSeq ID or GI accession numbers. KEGG contains all known metabolic pathways and a small number of regulatory pathways and transport mechanisms. The KEGG PATHWAY database contains graphical representations of pathways and lists of enzymes and reactions within the pathways. All specific pathway maps and overviews were manually drawn and contained links to additional information on pathway compounds, enzymes and genes. Pathway molecules from KEGG are identified by E.C. Numbers which are mapped to KEGG Gene IDs and then to UniProt IDs. The total count of initial pathways, proteins, compounds, protein complexes, and pathway interactions are shown in Figure 8.
<p>Figure 8</p>An overview of pathway data integration process
An overview of pathway data integration process. The figure shows the whole process of pathway data integration and the basic statistics of pathway data sources.
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Pathway data integration
We developed a model-driven approach for syntactic, semantic, and schematic level integrations of heterogeneous pathway data. Since pathway data were collected in a variety of formats, Python XML/HTML data parsers were developed to convert them into a common tab-delimited textual format to ensure syntactic level data compatibility. The semantic compatibility of the data was enforced by cleaning up data attributes and data values to keep them consistent, using a standard data extraction, transformation, and loading (ETL) process characteristics of data warehousing-based data integration approaches. All pre-processed data were parsed, cleaned, and loaded into data warehouse staging tables before reaching their final database table destinations. To maintain schematic data compatibilities, we model relationships among different pathway concepts using an entity-relationship (ER) data model (for more details on the data model, please refer to the documentation on the HPD Website and additional file 2). We further mapped all the involved proteins or genes to their UniProt Name Identifiers 69 and metabolites to their KEGG compound IDs before loading the HPD pathway data into data warehouse tables defined by the ER data model. All HPD molecular entities, events, and pathways were assigned unique HPD-specific identifiers.
Online HPD software design
The HPD database was developed as a data warehouse application. The online version of HPD is a standard 3-tier Web application, which consists of an Oracle 10 g database at the backend database server layer, Apache/PHP server scripts at the middleware application Web server layer, and CSS-driven Web pages presented at the browser.
Pathway similarity measure
The pathway similarity measure can be defined as the extent of overlaps, e.g., common number of genes/proteins, shared between two different pathways. We define a pathway-pathway similarity score Si, j based on equation (2) 70. Both overlap and similarity score values can be downloaded from HPD Website.
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Here, N denotes total number of pathways. Pi and Pj denote two different pathways, while |Pi| and |Pj| are the numbers of molecules that can be mapped to UniProt ID respectively in these two pathways. Their intersection Pi ∩ Pj denotes a common set of molecules that can be mapped to the same UniProt ID, while their union Pi ∪ Pj is calculated as |Pi| + |Pj| - |Pi ∩ Pj|. Here α is a weight coefficient among [0, 1], and we currently use α = 0.8 to count varying degree of contributions from calculations based both on the overlap (left item SL) and the cover (right item SR).
We can also make special considerations for subnetwork relationship (defined by the Nature Pathway Interaction database at http://pid.nci.nih.gov/. For subnetwork relationship, we define Si, j = 1.01, if pathway Pi has a subnetwork as Pj, and Si, j = -1.01 if pathway Pi is a subnetwork of Pj.